1 Towards a Synopsis Warehouse Peter J. Haas IBM Almaden Research Center San Jose, CA.
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Transcript of 1 Towards a Synopsis Warehouse Peter J. Haas IBM Almaden Research Center San Jose, CA.
1
Towards a Synopsis Warehouse
Peter J. Haas
IBM Almaden Research Center
San Jose, CA
2
Acknowledgements:
Kevin Beyer
Paul Brown
Rainer Gemulla (TU Dresden)
Wolfgang Lehner (TU Dresden)
Berthold Reinwald
Yannis Sismanis
3
Information Discovery for the Enterprise
Syndicated Data Provider Crawlable/deep WebCompany Data
Semi-Structured UnstructuredStructured
Office documentsE-Mail, Product Manuals
ECM (reports, spreadsheets,
Financial docs (XBRL))
ERP (SAP), CRM, WBIBPM, SCM
Business-Object Discovery
Query: “Explain the product movement, buyer behavior, maximize the ROI on my product campaigns.”
Query: “The sales team is visiting company XYZ next week. What do they need to know about XYZ?”
ContentMetadata
Business objects
Enterprise Repository
Analyze, IntegrateCrawl, ETL
Search BusinessIntelligence
Order
Account
CustomerData Analysis
&Similarity
4
Motivation, Continued• Challenge: Scalability
– Massive amounts of data at high speed• Batches and/or streams
– Structured, semi-structured, unstructured data
• Want quick approximate analyses– Automated data integration and schema discovery– “Business object” identification– Quick approximate answers to queries– Data browsing/auditing
• Our approach: a warehouse of synopses– For scalability and flexibility
5
A Synopsis Warehouse
Full-ScaleWarehouse Of Data Partitions
Synop.
Synop.
Synop.
S1,1 S1,2 Sn,mWarehouseof Synopses
merge
S*,* S1-2,3-7 etc
6
Outline
• Synopsis 1: Uniform samples– Background– Creating and combining samples
• Hybrid Bernoulli and Hybrid Reservoir algorithms– Updating samples
• Stable datasets: random pairing• Growing datasets: resizing algorithms• Maintaining Bernoulli samples of multisets
• Synopsis 2: AKMV samples for DV estimation– Base partitions: KMV synopses
• DV estimator and properties – Compound partitions: augmentation
• DV estimator and closure properties
7
Synopsis 1: Uniform Samples
• Design goals– True uniformity – Bounded memory– Keep sample full– Support for compressed samples
• 80% of 1000 customer datasets had < 4000 distinct values
Uniform Sample
Other Synopses
Mining AlgorithmsStratified Samples, Etc.
xx
xx
xx
xx
xx
xxx
xx
xx
xx
xx
Statistical
Procedures
8
Classical Uniform Methods
• Bernoulli sampling – Coin flip: includes each element with prob = q– Random, unbounded (binomial) sample size– Easy to merge: Bern(q) Bern(q) = Bern(q)
• Reservoir sampling– Creates uniform sample of fixed size k
• Insert first k elements into sample• Then insert ith element with prob. pi = k / i
– Variants and optimizations (e.g., Vitter)– Merging is harder
x6 x5 x4 x3 x2 x1
x4 x2 x1 Sample size = 3
Include with prob. 3/5
9
Drawback of Basic Methods
• Neither method is very compact – Ex: dataset = (<A,500>,<B,300>)– Stored as (A,A,…,A,B,B,…B) - 800 chars
• Concise sampling (GM 98)– Compact: purge Bern(q) sample S if too large
• Bern(q’/q) subsample of S Bern(q’) sample
– Not uniform (rare items under-represented)
10
New Sampling Methods (ICDE ’06)
• Two flavors: – Hybrid reservoir (HR)– Hybrid Bernoulli (HB)
• Properties– Truly uniform– Bounded footprint at all times– Will store exact distribution if possible– Samples stored in compressed form– Merging algorithms available
11
Hybrid Reservoir (HR) Sampling
{<a,3>}+a
{<a,3>,b}+b
{<a,3>,<b,2>}+b
+c
{a,<b,2>} (subsample)
{a,b,b} (expand)
{c,b,b} (reservoir sampling)
+d {c,b,d}
Ex: Sample capacity = two <v,#> pairs or three values
+a {a,a,a}
{<a,3>} (compress)
{<a,2>}+a +a
… …
done
Phase 1 (Maintain exact frequency distribution)
Phase 2 (Reservoir sampling)
{<a,3>,<b,1>}+b
12
Hybrid Bernoulli
• Similar to Hybrid Reservoir except– Expand into Bernoulli sample in Phase 2– Revert to Reservoir sample in Phase 3
• If termination in Phase 2– Uniform sample– “Almost” a Bernoulli sample
(controllable engineering approximation)
13
Merging Samples
• Both samples in Phase 2 (usual case)– Bernoulli: equalize q’s and take union
• Take subsample to equalize q’s
– Reservoir: take subsamples and merge• Random (hypergeometric) subsample size
• Corner cases– One sample in Phase 1, etc.– See ICDE ’06 paper for details
14
HB versus HR
• Advantages:– HB samples are cheaper to merge
• Disadvantages:– HR sampling controls sample size better– Need to know partition size in advance
• For subsampling during sample creation
– Engineering approximation required
15
Speedup: HB Sampling
You derive “speed-up” advantages from parallelism with up to about 100 partitions.
16
Speedup: HR Sampling
Similar results to previous slide, but merging HR samples is more complex than HB samples.
17
Linear Scale-Up
HB Sampling
HR Sampling
18
Updates Within a Partition• Arbitrary inserts/deletes (updates trivial)• Previous goals still hold
– True uniformity– Bounded sample size– Keep sample size close to upper bound
• Also: minimize/avoid base-data access
Sample
Partition
(updates), deletes, insertsFull-Scale
Warehouse
XExpensive!Synopsis
Warehouse
19
New Algorithms (VLDB ’06+)• Stable datasets: Random pairing
– Generalizes reservoir/stream sampling• Handles deletions• Avoids base-data accesses
– Dataset insertions paired randomly with “uncompensated deletions”• Only requires counters (cg, cb) of “good” and “bad” UD’s• Insert into sample with probability cb / (cb + cg)
– Extended sample-merging algorithm (VLDBJ ’07)
• Growing datasets: Resizing– Theorem: can’t avoid base-data access– Main ideas:
• Temporarily convert to Bern(q): may require base-data access• Drift up to new size (stay within new footprint at all times)• Choose q optimally to reduce overall resizing time
– Approximate and Monte Carlo methods
20
Bernoulli Samples of Multisets (PODS ’07)
• Bernoulli samples over multisets (w. deletions)– When boundedness is not an issue– Compact, easy to parallelize– Problem: how to handle deletions (pairing?)
• Idea: maintain “tracking counter”– # inserts into DS since first insertion into sample (GM98)
• Can exploit tracking counter– To estimate frequencies, sums, avgs
• Unbiased (except avg) and low variance
– To estimate # distinct values (!)
• Maintaining tracking counter– Subsampling: new algorithm – Merging: negative result
21
Outline
• Synopsis 1: Uniform samples– Background– Creating and combining samples
• Hybrid Bernoulli and Hybrid Reservoir algorithms– Updating samples
• Stable datasets: random pairing• Growing datasets: resizing algorithms• Maintaining Bernoulli samples of multisets
• Synopsis 2: AKMV samples for DV estimation– Base partitions: KMV synopses
• DV estimator and properties – Compound partitions: augmentation
• DV estimator and closure properties
22
AKMV Samples (SIGMOD ’07)
• Goal: Estimate # distinct values– Dataset similarity (Jaccard distance)– Key detection– Data cleansing
• Within warehouse framework– Must handle multiset union, intersection,
difference
23
KMV Synopsis• Used for a base partition• Synopsis: k smallest hashed values
– vs bitmaps (e.g., logarithmic counting)• Need inclusion/exclusion to handle intersection• Less accuracy, poor scaling
– vs sample counting• Random size K (between k/2 and k)
– vs Bellman [DJMS02]• minHash for k independent hash functions• O(k) time per arriving value, vs O(log k)
• Can view as uniform sample of DV’s
24
The Basic Estimator• Estimate:
– U(k) = kth smallest (normalized) hashed value
• Properties (theory of uniform order statistics)– Normalized hashed values “look like” i.i.d. uniform[0,1] RVs
• Large-D scenario (simpler formulas)– Theorem: U(k) approx.= sum of k i.i.d exp(D) random variables
– Analysis coincides with [Cohen97]– Can use simpler formulas to choose synopsis size
( )ˆ ( 1) / kD k U
( 1) ( 1)ˆ ˆ[ ] and [ ] ( 1)( 1)( 2) ( )
ˆ| | / is bounded in (can also get confidence bounds)
Asymptotically efficient as (minimal variance)
r r D D D rE D D E D k
k k k r
E D D D D
k
25
Compound Partitions
• Given a multiset expression E– In terms of base partitions A1,…,An
– Union, intersection, multiset difference
• Augmented KMV synopsis– KMV synopsis for
– Counters: cE(v) = multiplicity of value v in E
– AKMV synopses are closed under multiset operations
• Estimator (unbiased) for # DVs in E:
1 nA A
( )
1ˆ EE
k
K kD
k U
KE = # positive counters
26
Experimental Comparison
Unbiased SDLogLog Sample-Counting Unbiased-baseline0
0.02
0.04
0.06
0.08
0.1
Abs
olut
e R
elat
ive
Err
or
27
For More Details• "Toward automated large scale information integration and discovery." P. Brown,
P. J. Haas, J. Myllymaki, H. Pirahesh, B. Reinwald, and Y. Sismanis. In Data Management in a Connected World, T. Härder and W. Lehner, eds. Springer-Verlag, 2005.
• “Techniques for warehousing of sample data”. P. G. Brown and P. J. Haas. ICDE ‘06.
• “A dip in the reservoir: maintaining sample synopses of evolving datasets”. R. Gemulla, W. Lehner, and P. J. Haas. VLDB ‘06.
• “Maintaining Bernoulli samples over evolving multisets”. R. Gemulla, W. Lehner, and P. J. Haas. PODS ‘07.
• “On synopses for distinct-value estimation under multiset operations” K. Beyer, P. J. Haas, B. Reinwald, Y. Sismanis, and R. Gemulla. SIGMOD ‘07.
• “Maintaining bounded-size sample synopses of evolving multisets” R. Gemulla, W. Lehner, P. J. Haas. VLDB Journal, 2007.
28
Backup Slides
29
Bernoulli Sampling– Bern(q) independently includes each element with probability q– Random, uncontrollable sample size– Easy to merge Bernoulli samples: union of 2 Bern(q) samp’s = Bern(q)
+t1
2/3 1/3
1
+t2 2
2/3 1/3
21 1
2/3 1/3
30%
1 23 2 32 1 31 1 2 3
2/3 1/32/3 1/3 2/3 1/3 2/3 1/3
+t315% 15% 7% 15% 7% 7% 4%
q = 1/3
30
Reservoir Sampling (Example)
• Sample size M = 2
1 2+t1 +t2100%
1 2
1/3
3 2 1 3
1/3 1/3
1 2+t1 +t2
+t333% 33% 33%
t 1 2 4 2 1 4
2/4 1/4 1/4
3 2 4 2 3 4
2/4 1/4 1/4
1 3 4 3 1 4
2/4 1/4 1/4
+ 4
1 2
1/3
3 2 1 3
1/3 1/3
1 2+t1 +t2
+t3
16% 8% 8% 8% 8% 8% 8%16% 16%
31
Concise-Sampling Example
• Dataset – D = { a, a, a, b, b, b }
• Footprint– F = one <value, #> pair
• Three (possible) samples of size = 3– S1 = { a, a, a }, S2 = { b, b, b }, S3 = { a, a, b }.– S1 = {<a,3>}, S2 = {<b,3>}, S3 = {<a,2>,<b,1>}.
• Three samples should have equal likelihood – But Prob(S1) = Prob(S2) > 0 and Prob(S3) = 0
• In general:– Concise sampling under-represents ‘rare’ population
elements
32
Hybrid Bernoulli Algorithm• Phase 1
– Start by storing 100% sample compactly– Termination in Phase 1 exact distribution
• Abandon Phase 1 if footprint too big– Take subsample and expand– Fall back to Bernoulli sampling (Phase 2)– If footprint exceed: revert to reservoir sampling (Phase 3)
• Compress sample upon termination• If Phase 2 termination: (almost) Bernoulli sample• If Phase 3 termination: Bounded reservoir sample• Stay within footprint at all times
– Messy details
33
Subsampling in HB Algorithm
• Goal: find q such that P{|S| > nF} = p
• Solve numerically:
• Approximate solution (< 3% error):
| | | || |
1(1 )
F
D j D jD
j n jq q p
2 2 2
2
| | 2 | | | | 4 | | 4
2 | | | |
F p p p F F
p
D n z z D D z D n nq
D D z
34
Merging HB Samples
• If both samples in Phase 2– Choose q as before (w.r.t. |D1 U D2|)
– Convert both samples to compressed Bern(q)
[Use Bern(q’/q) trick as in Concise Sampling]– If union of compressed samples fits in memory
then join and exitelse use reservoir sampling (unlikely)
35
Merging a Pair of HR Samples
• If both samples in Phase 2– Set k = min(|S1|, |S2|)
– Select L elements from S1 and k – L from S2
• L has hypergeometric distribution on {0,1,…,k}– Distribution depends on |D1|, |D2|
• Take (compressed) reservoir subsamples of S1, S2
• Join (compressed union) and exit
36
Generating Realizations of LL is a random variable with probability mass function
P(l) = P{ L=l } given by:
k
DD
lk
D
l
D
lP||||
||||
)(21
21
for l = 0, 1, …. k-1
• Simplest implementation– Compute P recursively– Use inversion method (probe cumulative distribution at each merge)
• Optimizations when |D|’s and |S|’s unchanging– Use alias methods to generate L from cached distributions in O(1) time
37
Naïve/Prior Approaches
unstableconduct deletions, continue with smaller sample
(RS with deletions)
CommentsTechniqueAlgorithm
expensive, low space efficiency in our setting
tailored for multiset populations Distinct-value sampling
special case of our RP algorithm
developed for data streams (sliding windows only)
Passive sampling
Not uniform (!)“coin flip” sampling with deletions, purge if too large
Bernoulli sampling with purging
stable but expensiveimmediately sample from base data to refill the sample
CAR(WOR)
expensive, unstablelet sample size decrease, but occasionally recompute
RS with resampling
not uniformuse insertions to immediately refill the sample
Naïve
Counting samples Not uniformModification of concise sampling
38
Random Pairing
1 2
1 2
1/3
3 2 1 3
1/3 1/3
+t1 +t2
+t3
-t2 1 3 1 3
-t3 1 1
1
1
1
1
1
1
1 2
1 2
1/3
3 2 1 3
1/3 1/3
+t1 +t2
+t3
-t2 1 3 1 3
-t3 1 1
+t4
1
1
1
1
1
1
1 1 4
1/2 1/2
4 4
1/2 1/2
1 4 1
1/2 1/2
1 2
1 2
1/3
3 2 1 3
1/3 1/3
+t1 +t2
+t3
-t2 1 3 1 3
-t3 1 1
+t4
1
1
1
1
1
1
+t5
1 1 4
1/2 1/2
1 4 1
1/2 1/2
4 4
1/2 1/2
1 5
1
1 4
1
1 5
1
1 4
1
4 5
1
4 5
1
16% 16% 16% 16%16% 16%
39
Performance
0 2 4 6 8 1 0
R PC A R
C A R W O RR S RB S P
0.1
1
10
100
100K sam p le10 m illion ope ra tions
D ataset S ize (m illions)
Co
st
40
A Negative Result• Theorem
– Any resizing algorithm MUST access base data
• Example– data set
– samples of size 2
– new data set
– samples of size 3
1 2 3 4
1 2 1 3 1 4 2 3 2 4 3 4
16% 16% 16% 16% 16% 16%
1 2 3 1 2 5
0% >0%Not uniform!
1 2 3 4 5 6 ...
41
Resizing: Phase 1
Conversion to Bernoulli sample– Given q, randomly determine sample size
• U = Binomial(|D|,q)
– Reuse S to create Bernoulli sample• Subsample if U < |S|• Else sample additional tuples (base data access)
– Choice of q• small less base data accesses• large more base data accesses
42
Resizing: Phase 2
Run Bernoulli sampling– Include new tuples with probability q– Delete from sample as necessary– Eventually reach new sample size– Revert to reservoir sampling – Choice of q
• small long drift time• large short drift time
43
Choosing q (Inserts Only)
• Expected Phase 1 (conversion) time
• Expected Phase 2 (drifting) time
• Choose q to minimize E[T1] + E[T2]
1
| |[ ] | | ln
| | min(| | , ')a
D ME T t D
D M D q M M
2
' | |[ ] bt M D q
E Tq
44
Resizing Behavior
• Example (dependence on base-access cost):– resize by 30% if sampling fraction drops below 9%– dependent on costs of accessing base data
Low costs
immediate resizing
Moderate costs
combined solution
High costs
degenerates to Bernoulli sampling
45
Choosing q (w. Deletes)
• Simple approach (insert prob. = p > 0.5)– Expected change in partition size (Phase 2)
• (p)(1)+(1-p)(-1) = 2p-1
– So scale Phase 2 cost by 1/(2p-1)
• More sophisticated approach– Hitting time of Markov chain to boundary– Stochastic approximation algorithm
• Modified Kiefer-Wolfowitz
46
The RPMerge Algorithm
• Conceptually: defer deletions until after merge
• Generate Yi’s directly – Can assume that
deletions happen after the insertions
1
7
7
2
34
4
5
5
6
R1+
S1+
S+
S
R1
RP( = 4)M1
RP( ) 1-
2-
d1 = 28
12
12
911
11
5
5 11
11
10
1 0
13
13
14
14
1
5 11
1
1
14
14
R2+
S2+
R2 d2 = 3
X1 = 2
Y1 = 2 Y2 = 1
X2 = 2
RP( = 5)M2
47
New Maintenance Method• Idea: use tracking counters
– After j-th transaction, augmented sample Sj is
Sj = { (Xj (t),Yj (t)): t T and Xj (t) > 0}• Xj(t) = frequency of item t in the sample
• Yj(t) = net # of insertions of t into R since t joined sample
Deletion of t
Delete t from sample
With prob. (Xj(t) – 1) / (Yj(t) – 1)
Sample
Data
Xj(t) copies of item t in sample
Nj(t) copies of item t in dataset
Insertion of t
Insert t into sample
With prob. q
48
Frequency Estimation• Naïve (Horvitz-Thompson) unbiased estimator
• Exploit tracking counter:
• Theorem
• Can extend to other aggregates (see paper)
1 1ˆiX
iiXN
q
X
1 if 0ˆ0 if
1
0i
iY
i
i q YN
Y
Y
andˆ ˆ ˆ[ ] [ ] [ ]i i iY i Y XE N N V N V N
49
Estimating Distinct-Value Counts
• If usual DV estimators unavailable (BH+07)• Obtain S’ from S: insert t D(S) with probability
• Can show: P(t S’) = q for t D(R)• HT unbiased estimator: = |S’| / q• Improve via conditioning (Var[E[U|V]] ≤ Var[U]):
1 if ( ) 1( )
if ( ) 1
Y tp t
q Y t
ˆHTD
( )ˆ ˆ[ | ] ( ) /Y HT t D S
D E D S p t q
50
Estimating the DV Count
• Exact computation via sorting– Usually infeasible
• Sampling-based estimation– Very hard problem (need large samples)
• Probabilistic counting schemes– Single-pass, bounded memory– Several flavors (mostly bit-vector synopses)
• Linear counting (ASW87)• Logarithmic counting (FM85,WVT90,AMS, DF03)• Sample counting (ASW87,Gi01, BJKST02)
51
Intuition
• Look at spacings– Example with k = 4 and D = 7:
– E[V] 1 / D so that D 1 / E[V]
– Estimate D as 1 / Avg(V1,…,Vk)
– I.e., as k / Sum(V1,…,Vk)
– I.e., as k / u(k)
– Upward bias (Jensen’s inequality) so change k to k-1
x x x x x x x
V1 V 2 V 3 V4
0 1